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🔬🧬 Excited to share our latest study Science Magazine developing ChromExM which uses expansion microscopy to visualize the chromatin, transcription, RNA POL II and pioneer factor Nanog during genome activation.

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Giraldez Lab

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136,261 次观看 • 3 年前 •via X (Twitter)

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Giraldez Lab 的头像
Giraldez Lab3 年前

Great collaborative work with @bewersdorflab and @pioneerfactors congratulations to @MarkPownall5, @MiaoLiyun and all authors

Giraldez Lab 的头像
Giraldez Lab3 年前

Thank you @ScienceMagazine and @Cell_Tokyo_Tech Hiroshi Kimura @tim_stasevich for a great prospective on our work

Sanjeev Galande 的头像
Sanjeev Galande3 年前

@JShendure @ScienceMagazine @MarkPownall5 @bewersdorflab @MiaoLiyun @pioneerfactors @YaleGenetics @CBBonFOX @ceVejnar @OnsMSaad1 @MariaBeOr @alice_sherrard Exciting findings and awesome imaging, congratulations!

Juan Pablo Fernandez 的头像
Juan Pablo Fernandez3 年前

@ScienceMagazine @MarkPownall5 @bewersdorflab @MiaoLiyun @pioneerfactors @YaleGenetics @CBBonFOX @ceVejnar @OnsMSaad1 @MariaBeOr @alice_sherrard Congrats team! @GiraldezLab, @MiaoLiyun , @ceVejnar. Amazing work!! 👏

Justin Crocker 的头像
Justin Crocker3 年前

@ScienceMagazine @MarkPownall5 @bewersdorflab @MiaoLiyun @pioneerfactors @YaleGenetics @CBBonFOX @ceVejnar @OnsMSaad1 @MariaBeOr @alice_sherrard Awesome paper!

Takashi Fukaya 的头像
Takashi Fukaya3 年前

@ScienceMagazine @MarkPownall5 @bewersdorflab @MiaoLiyun @pioneerfactors @YaleGenetics @CBBonFOX @ceVejnar @OnsMSaad1 @MariaBeOr @alice_sherrard Beautiful work, congratulations!!

Junjie Guo 郭俊傑 的头像
Junjie Guo 郭俊傑3 年前

@JeanJuChung @ScienceMagazine @MarkPownall5 @bewersdorflab @MiaoLiyun @pioneerfactors @YaleGenetics @CBBonFOX @ceVejnar @OnsMSaad1 @MariaBeOr @alice_sherrard Amazing work! Congrats!!

monika piwecka 的头像
monika piwecka3 年前

@ScienceMagazine @MarkPownall5 @bewersdorflab @MiaoLiyun @pioneerfactors @YaleGenetics @CBBonFOX @ceVejnar @OnsMSaad1 @MariaBeOr @alice_sherrard Added to my must-read list! Also, i can not stop watching that video! 🤩

Marina Chekulaeva 的头像
Marina Chekulaeva3 年前

@monika_piwecka @ScienceMagazine @MarkPownall5 @bewersdorflab @MiaoLiyun @pioneerfactors @YaleGenetics @CBBonFOX @ceVejnar @OnsMSaad1 @MariaBeOr @alice_sherrard Congrats Antonio, impressive!

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Excited to share our work showing how the Integrator complex terminates RNA polymerase II (Pol II) transcription i.e. removal of Pol II from the DNA template Published in nature Many thanks to my mentor Patrick Cramer Patrick Cramer for unwavering support
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Excited to share our work showing how the Integrator complex terminates RNA polymerase II (Pol II) transcription i.e. removal of Pol II from the DNA template Published in nature Many thanks to my mentor Patrick Cramer Patrick Cramer for unwavering support

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Discover our latest study, published in Science Magazine , which reveals a groundbreaking approach to regenerating axons through complete spinal cord injury, reversing paralysis. Read the full article here:
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Professor Robert Clancy discusses "the scary bit". The "Third Wave" of the mass Global mRNA experiment. He tells Dr. John Campbell about the case study of an 80 year old women who "had 9 messenger RNA vaccines" with stable breast cancer had "secondaries in her skin [which were biopsied]." "In the nucleus of the cancer cells, and the cytoplasm, they found spike protein". This discussion partly relates to the "Bait and Switch", where Pfizer swapped out process 1 for process 2, paving the way for mod RNA injections with DNA contamination and SV40 promoters to go into billions of arms. Some of the mechanisms discussed include suppression of P53, activation of the cGAS-STING pathway, and "Reverse Transcription".

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Our latest AI integration equips Reform with a causality-focused ML model that uncovers the mechanisms driving relationships between variables. By employing advanced algorithms, this method can detect “confounders”. A confounder is a variable that influences both the cause and effect variables being studied, which can distort the apparent relationship between them. The system uses a machine learning approach to identify and remove these confounders, which reveals genuine causal effects. This approach provides Reform with a more robust foundation for decision-making, enabling more effective guidance that addresses the root causes rather than superficial correlations. In addition to this, the AI integration includes models for forecasting precursor events and predicting prices, among other methods that enhance the system’s capabilities. Interested in diving deeper into these methods? Read our latest case study today.

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40,232 次观看 • 2 年前

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OFFICIAL ANNOUNCEMENT FROM THE DEV TEAM 🚨 The result of our Hackathon SendAI journey! After 13 days of focused development during the Solana AI Hackathon, we’re excited to share a teaser video showcasing ELYRAI. This video highlights the features we’ve integrated, which are now live on ELYRAI and accessible via our website. We’re also thrilled to announce the integration of key sponsors into our AI agents : 1️⃣ ai16zdao with Eliza OS, powering enhanced AI functionalities. ( Shaw ) 2️⃣ assisterr, powering our website chat and web extensions, integrating Elyrai into dApps. ( Nick Havryliak ) 3️⃣ Jupiter , guiding users on their platform through the ELYRAI web extension and a widget integrated into our website. ( meow Jakey ) Special thanks to Superteam France for their support during this hackathon ( Arthur Dietrich , Cptn | Ferno 🔥 Arnaud )

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14,625 次观看 • 1 年前

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🚀 We're thrilled to introduce Orthrus 🧬🐕—a groundbreaking mature RNA foundation model designed to push the boundaries of RNA property prediction! 🔬 What is Orthrus? Orthrus is a Mamba-based RNA foundation model, pre-trained using a novel self-supervised contrastive learning objective with biologically inspired augmentations. It optimizes the similarity between splicing isoforms and orthologous transcripts, capturing functional and evolutionary relationships to enhance mature RNA property prediction accuracy. 📑 Preprint: 💻 Code: 🌐 Project Page: 📦 Model Weights: 🧠 Why Orthrus? Decoding the RNA regulatory code is key to understanding biology, but traditional experimental approaches are slow and costly. Existing genomic foundation models rely on techniques like masked language modeling or next-token prediction, which aren't fully aligned with the complexities of genomic data—leading to suboptimal results. 🌟 Orthrus Highlights: - Biologically-Informed Contrastive Learning 🧪: A novel contrastive learning objective designed specifically for genomics, maximizing similarity between splicing isoforms and orthologous transcripts across species. - Extensive Pre-training 📊: Trained on splicing annotations from 10 species and orthologous alignments from 400+ mammalian species (Zoonomia Project), with a focus on sequences of high functional importance. - Superior Representations🏅: Orthrus outperforms existing genomic models on 5 mRNA property prediction tasks, often surpassing supervised methods with just a simple linear transformation. - Efficiency in Low-Data Settings📉: Orthrus excels in low-data regimes, achieving state-of-the-art results with as few as 45 labeled examples for fine-tuning on RNA half-life prediction. Shoutout to the amazing leading authors Phil (Phil Fradkin) and Ian (Ian Shi)! Also the work is impossible without an outstanding collaboration by Karina (Karin(a) Isaev), Brendan (Brendan Frey) , Quaid (Quaid Morris), Leo J. Lee! Vector Institute University Health Network U of T Department of Computer Science Temerty Centre for AI in Medicine (T-CAIREM) Department of Laboratory Medicine & Pathobiology

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114,994 次观看 • 2 年前

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Our latest work is out today in Science Magazine: a self-organizing system that models organizer-driven patterning and commissural axon guidance in human neural development. This was achieved with human midline #assembloids by combining floor plate and spinal cord #organoids from pluripotent stem cells. A key advance is the three-part configuration (a floor plate squeezed in between two spinal cords), which enables human axons to cross the midline (and the subsequent synchronization of activity between the two parts seen in the video below). This platform opens the door to many applications; here, we used it to screen and identify human-enriched midline crossing regulators. Work led by Massimo Onesto with Neal Amin

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10,375 次观看 • 1 年前